Metadata-Version: 2.1
Name: time-series-model-basics
Version: 0.0.3
Summary: Put a description
Home-page: https://github.com/kikejimenez/time_series_model_basics/tree/master/
Author: Enrique Jimenez
Author-email: physieira@gmail.com
License: Apache Software License 2.0
Keywords: data analytics forecasting
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: pip
Requires-Dist: packaging
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: numba
Requires-Dist: matplotlib
Requires-Dist: plotly
Requires-Dist: sklearn
Requires-Dist: kaleido
Requires-Dist: scikit-optimize
Provides-Extra: dev

# Forecasting Basics
> <a href='https://github.com/kikejimenez/time_series_model_basics'>Documentation and Code is hosted on Github</a>  


> Generate and Plot Forecasts for Time Series Data 

> Includes:SMA, WMA and Single and Double Exponential Smoothing 

## Install

```bash
pip install time-series-model-basics
```

## Simple Moving Average

Plot a Simulated Time Series with two or  any number of Simple Moving Averages as follows:

```python
from time_series_model_basics.moving_average import SMA

df, fig = SMA(1, 4)

fig.write_image("images/sma.png")
```

<img src="https://raw.githubusercontent.com/kikejimenez/time_series_model_basics/master/nbs/images/sma.png" width="700" height="400" style="max-width: 700px">

When running on a notebook you may alternatively use
```python
fig.show()
```

Forecast with dataframe as follows:

```python
import pandas as pd

df = pd.read_csv(
    '../data/Electric_Production.csv',
    index_col='DATE',
    parse_dates=['DATE'],
)
ts_col = 'Electric Production'
df.columns = [ts_col]
_, fig = SMA(
    4,
    df=df,
    ts_col=ts_col,
)
fig.update_layout(
    autosize=False,
    width=1100,
    height=450,
)
fig.update_traces(line=dict(width=0.8))
fig.write_image("images/elec_prod_sma.png",)
```

<img src="https://raw.githubusercontent.com/kikejimenez/time_series_model_basics/master/nbs/images/elec_prod_sma.png" width="700" height="400" style="max-width: 700px">

## Weighted Moving Average

For the case of  Weighted Moving Averages, pass the weights as lists:

```python
from time_series_model_basics.moving_average import WMA

df,fig = WMA([1,1,2],[3,2])

fig.write_image("images/wma.png")
```

<img src="https://raw.githubusercontent.com/kikejimenez/time_series_model_basics/master/nbs/images/wma.png" width="700" height="400" style="max-width: 700px">

## Simple Smoothing 

Plot a Simulated Time Series with two or  any number of simple exponential smoothing as follows:

```python
from time_series_model_basics.smoothing import SINGLE

df, fig = SINGLE(.15, .5)
fig.write_image("images/single.png",)
```

<img src="https://raw.githubusercontent.com/kikejimenez/time_series_model_basics/master/nbs/images/single.png" width="700" height="400" style="max-width: 700px">

## Double Smoothing 

```python
from time_series_model_basics.smoothing import DOUBLE

df, fig = DOUBLE(
    [.25, .3],
    [.5, .6],
)
fig.write_image("images/double.png")
```

<img src="https://raw.githubusercontent.com/kikejimenez/time_series_model_basics/master/nbs/images/double.png" width="700" height="400" style="max-width: 700px">

## Author

- Enrique Jimenez


